Neural prosthetics use neural signals to restore lost motor function to patients who suffer from motor neurological injury or disease. Work has been done to develop so-called “continuous decoders,” which use neural signals to estimate the kinematics of a neural prosthetic device. However, decoding fundamentally different “discrete action state” signals could be crucial to enable higher performing, clinically relevant neural prosthetics. For example, when using a computer cursor, its utility is vastly limited if it is only capable of moving around on the screen. Instead, the cursor must also has to be able to make selections, i.e. “clicking,” to be practically useful. Analogously, when controlling a robotic arm and hand, the utility of the device would be greatly enabled by allowing the hand to “grasp” objects, or adopt other typical hand configurations. The present invention addresses the need for decoding discrete action states based on neural signals for the control of prosthetic devices.